Focused on the census data from tidycensus. API key required via census.gov.
library(tidyverse)
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library(tidycensus)
library(tigris)
To enable
caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
Attaching package: ‘tigris’
The following object is masked from ‘package:graphics’:
plot
get_decennial requires the geography (level of aggregation) and variables parameters.
get_acs - American Community Survey data - based on samples, or estimates of the population. Dataframes returned from this function will have estimates and moe (margin of error for 90% CI).
# Obtain and view state populations from the 2010 US Census
state_pop <- get_decennial(geography = "state", variables = "P001001")
Getting data from the 2010 decennial Census
state_pop
# Obtain and view state median household income from the 2012-2016 American Community Survey
state_income <- get_acs(geography = "state", variables = "B19013_001")
Getting data from the 2012-2016 5-year ACS
state_income
Geography regions - legal entities (e.g. country) - statistical etities (e.g. tract)
tidycensus docs include geography available tidycensus "returns data in tidy foramt, but can return data in wide format with the output = "wide" parameter.
# Get an ACS dataset for Census tracts in Texas by setting the state
# this is the median household income
tx_income <- get_acs(geography = "tract",
variables = c(hhincome = "B19013_001"),
state = "TX")
Getting data from the 2012-2016 5-year ACS
Using FIPS code '48' for state 'TX'
# Inspect the dataset
head(tx_income)
# Get an ACS dataset for Census tracts in Travis County, TX
travis_income <- get_acs(geography = "tract",
variables = "B19013_001",
state = "WA",
county = "King")
Getting data from the 2012-2016 5-year ACS
Using FIPS code '53' for state 'WA'
Using FIPS code '033' for 'King County'
# Inspect the dataset
head(travis_income)
# Return county data in wide format
or_wide <- get_acs(geography = "county",
state = "OR",
variables = c(hhincome = "B19013_001",
medage = "B01002_001"),
output = "wide")
Getting data from the 2012-2016 5-year ACS
Using FIPS code '41' for state 'OR'
# Compare output to the tidy format from previous exercises
head(or_wide)
# Create a scatterplot
plot(or_wide$hhincomeE, or_wide$medageE)
To find census variables, look at online resources like Census Reporter - https://censusreporter.org/A
load_variables helps to find variables. takes year, dataset (acs5) and cache boolean, this can then be explored. Returns name, label, and concept fields.
ACS Variable structure B19001_002e B is a base (most data), (C collapsed, DP data profile, or S subject) 19001 is the table ID 002 the variable code within the table E is the estimate (optional in tidycensus, returns both the Estimate and Margin of error)
# Load variables from the 2012-2016 ACS
v16 <- load_variables(year = 2016,
dataset = "acs5",
cache = TRUE)
# Get variables from the ACS Data Profile
v16p <- load_variables(year = 2016,
dataset = "acs5/profile",
cache = TRUE)
# Set year and dataset to get variables from the 2000 Census SF3
v00 <- load_variables(year = 2000,
dataset = "sf3",
cache = TRUE)
# Filter for table B19001
filter(v16, str_detect(name, "B19001"))
# Use public transportation to search for related variables
filter(v16p, str_detect(label, fixed("public transportation",
ignore_case = TRUE)))
get_acs(geography = "county", state = "WA", year = 2016,
variables = "DP03_0021P", survey = "acs1") %>%
mutate(NAME = gsub(" County, Washington", "", NAME)) %>%
ggplot(aes(estimate, reorder(NAME, estimate))) +
geom_point(size = 3, color = "navy") +
geom_errorbarh(aes(xmax = estimate + moe, xmin = estimate - moe)) +
scale_x_continuous(labels = scales::percent_format(scale = 1, accuracy = 1)) +
hrbrthemes::theme_ipsum() +
labs(title = "Percent of workers commuting via public transit",
caption = "2016 ACS",
x = "Percent",
y = "County")
Getting data from the 2016 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using the ACS Data Profile
Using FIPS code '53' for state 'WA'
get_acs and get_decennial have an optional table param that allows all the variables to be fetched at once without specifying a variable param.
# Download table "B19001"
wa_income <- get_acs(geography = "county",
state = "WA",
table = "B19001")
Getting data from the 2012-2016 5-year ACS
Using FIPS code '53' for state 'WA'
# Check out the first few rows of wa_income
wa_income
# Assign Census variables vector to race_vars
race_vars <- c(White = "B03002_003", Black = "B03002_004", Native = "B03002_005",
Asian = "B03002_006", HIPI = "B03002_007", Hispanic = "B03002_012")
# Request a summary variable from the ACS
ca_race <- get_acs(geography = "county",
state = "WA",
variables = race_vars,
summary_var = "B03002_001")
Getting data from the 2012-2016 5-year ACS
Using FIPS code '53' for state 'WA'
# Calculate a new percentage column and check the result
ca_race_pct <- ca_race %>%
mutate(pct = 100 * (estimate / summary_est))
ca_race_pct
# Group the dataset and filter the estimate
ca_largest <- ca_race %>%
group_by(GEOID) %>%
filter(estimate == max(estimate))
head(ca_largest)
# Group the dataset and get a breakdown of the results
ca_largest %>%
group_by(variable) %>%
tally()
# Use a tidy workflow to wrangle ACS data
wa_grouped <- wa_income %>%
filter(variable != "B19001_001") %>%
mutate(incgroup = case_when(
variable < "B19001_008" ~ "below35k",
variable < "B19001_013" ~ "35kto75k",
TRUE ~ "above75k"
)) %>%
group_by(NAME, incgroup) %>%
summarize(group_est = sum(estimate))
wa_grouped
# Map through ACS1 estimates to see how they change through the years
mi_cities <- map_df(2012:2016, function(x) {
get_acs(geography = "place",
variables = c(totalpop = "B01003_001"),
state = "MI",
survey = "acs1",
year = x) %>%
mutate(year = x)
})
Getting data from the 2012 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
Getting data from the 2013 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
Getting data from the 2014 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
Getting data from the 2015 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
Getting data from the 2016 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
mi_cities %>% arrange(NAME, year)
# Get data on elderly poverty by Census tract in Vermont
vt_eldpov <- get_acs(geography = "tract",
variables = c(eldpovm = "B17001_016",
eldpovf = "B17001_030"),
state = "VT")
Getting data from the 2012-2016 5-year ACS
Using FIPS code '50' for state 'VT'
Error in curl::curl_fetch_memory(url, handle = handle) :
Operation was aborted by an application callback
# Group the dataset and calculate a derived margin of error
vt_eldpov2 <- vt_eldpov %>%
group_by(GEOID) %>%
summarize(
estmf = sum(estimate),
moemf = moe_sum(moe= moe, estimate = estimate)
)
# Filter rows where newly-derived margin of error exceeds newly-derived estimate
moe_check2 <- filter(vt_eldpov2, moemf > estmf)
# Check proportion of rows where margin of error exceeds estimate
nrow(moe_check2) / nrow(vt_eldpov2)
[1] 0.5815217
# Request median household income data
maine_inc <- get_acs(geography = "county",
variables = c(hhincome = "B19013_001"),
state = "ME")
Getting data from the 2012-2016 5-year ACS
Using FIPS code '23' for state 'ME'
# Generate horizontal error bars with dots
ggplot(maine_inc, aes(x = estimate, y = NAME)) +
geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) +
geom_point()
# Remove unnecessary content from the county's name
maine_inc2 <- maine_inc %>%
mutate(NAME = str_replace(NAME, " County, Maine", ""))
# Build a margin of error plot incorporating your modifications
ggplot(maine_inc2, aes(x = estimate, y = reorder(NAME, estimate))) +
geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) +
geom_point(size = 3, color = "darkgreen") +
theme_grey(base_size = 14) +
labs(title = "Median household income",
subtitle = "Counties in Maine",
x = "ACS estimate (bars represent margins of error)",
y = "") +
scale_x_continuous(labels = scales::dollar)
TIGER - Topologically integrated geologically encodind databse
tigris downloads line and shapes from Census Beuro and pulls them into R as Spatial objects.
Include geographic and tract regions. Also includes features such as roads and water features.
library(tigris)
To enable
caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
Attaching package: ‘tigris’
The following object is masked from ‘package:graphics’:
plot
# Get a counties dataset for Colorado and plot it
co_counties <- counties(state = "CO")
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Using FIPS code '08' for state 'CO'
head(co_counties)
An object of class "SpatialPolygonsDataFrame"
Slot "data":
Slot "polygons":
[[1]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -106.28156 38.08055
Slot "area":
[1] 0.8431487
Slot "hole":
[1] FALSE
Slot "ringDir":
[1] 1
Slot "coords":
[,1] [,2]
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[ reached getOption("max.print") -- omitted 1396 rows ]
Slot "plotOrder":
[1] 1
Slot "labpt":
[1] -107.67615 37.76404
Slot "ID":
[1] "227"
Slot "area":
[1] 0.1028336
Slot "plotOrder":
[1] 1 3 5 4 2 6
Slot "bbox":
min max
x -108.38107 -102.0448
y 36.99961 41.0026
Slot "proj4string":
CRS arguments:
+proj=longlat +ellps=GRS80
+towgs84=0,0,0,0,0,0,0 +no_defs
# Get a Census tracts dataset for Denver County, Colorado and plot it
denver_tracts <- tracts(state = "CO", county = "Denver")
Using FIPS code '08' for state 'CO'
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Using FIPS code '031' for 'Denver County'
plot(denver_tracts)
king_tracts <- tracts(state="WA", county = "King")
Using FIPS code '53' for state 'WA'
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Using FIPS code '033' for 'King County'
plot(king_tracts)
# Plot area water features for Lane County, Oregon
lane_water <- area_water(state = "OR", county = "Lane")
Using FIPS code '41' for state 'OR'
Using FIPS code '039' for 'Lane County'
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plot(lane_water)
king_water <- area_water(state = "WA", county = "King")
Using FIPS code '53' for state 'WA'
Using FIPS code '033' for 'King County'
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plot(king_water)
Looking at structure of the Spatial (sp) objects
# Check the class of the data
class(co_counties)
[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"
# Take a look at the information in the data slot
head(co_counties@data)
# Check the coordinate system of the data
co_counties@proj4string
CRS arguments:
+proj=longlat +ellps=GRS80
+towgs84=0,0,0,0,0,0,0 +no_defs
Difference between cartogragphic and tigris boundaries
# Get a counties dataset for Michigan
mi_tiger <- counties("MI")
Using FIPS code '26' for state 'MI'
# Get the equivalent cartographic boundary shapefile
mi_cb <- counties("MI", cb = TRUE)
Using FIPS code '26' for state 'MI'
# Overlay the two on a plot to make a comparison
plot(mi_tiger$geometry)
plot(mi_cb$geometry, add = TRUE, border = "red")
# Get a historic Census tract shapefile from 1990 for Williamson County, Texas
williamson90 <- tracts(state = "TX", county = "Williamson",
cb = TRUE, year = 1990)
Using FIPS code '48' for state 'TX'
Using FIPS code '491' for 'Williamson County'
# Compare with a current dataset for 2016
williamson16 <- tracts(state = "TX", county = "Williamson",
cb = TRUE, year = "2016")
Using FIPS code '48' for state 'TX'
Using FIPS code '491' for 'Williamson County'
# Plot the geometry to compare the results
par(mfrow = c(1, 2))
plot(williamson90$geometry)
plot(williamson16$geometry)
# Get Census tract boundaries for Oregon and Washington
or_tracts <- tracts("OR", cb = TRUE)
Using FIPS code '41' for state 'OR'
wa_tracts <- tracts("WA", cb = TRUE)
Using FIPS code '53' for state 'WA'
# Check the tigris attributes of each object
attr(or_tracts, "tigris")
[1] "tract"
attr(wa_tracts, "tigris")
[1] "tract"
# Combine the datasets then plot the result
or_wa_tracts <- rbind_tigris(or_tracts, wa_tracts)
plot(or_wa_tracts$geometry)
Tidy patterns can also be applied to combine multiple tigris (sf) objects together.
# Generate a vector of state codes and assign to new_england
new_england <- c("ME", "NH", "VT", "MA")
# Iterate through the states and request tract data for state
ne_tracts <- map(new_england, function(x) {
tracts(state = x, cb = TRUE)
}) %>%
rbind_tigris()
Using FIPS code '23' for state 'ME'
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Using FIPS code '33' for state 'NH'
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Using FIPS code '25' for state 'MA'
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plot(ne_tracts$geometry)
tracts(state = "WA", house = "upper")
Using FIPS code '53' for state 'WA'
Error in load_tiger(url, tigris_type = "tract", ...) :
unused argument (house = "upper")
# Get boundaries for Texas and set the house parameter
tx_house <- state_legislative_districts(state = "TX", house = "lower", cb = TRUE)
Using FIPS code '48' for state 'TX'
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# Merge data on legislators to their corresponding boundaries
tx_joined <- left_join(tx_house, tx_members, by = c("NAME" = "District"))
Error in check_join(x, y) : object 'tx_members' not found
# Plot the legislative district boundaries
ggplot(tx_joined) +
geom_sf()
# Set fill aesthetic to map areas represented by Republicans and Democrats
ggplot(tx_joined, aes(fill = Party)) +
geom_sf()
# Set values so that Republican areas are red and Democratic areas are blue
ggplot(tx_joined, aes(fill = Party)) +
geom_sf() +
scale_fill_manual(values = c("R" = "red", "D" = "blue"))
# Draw a ggplot without gridlines and with an informative title
ggplot(tx_joined, aes(fill = Party)) +
geom_sf() +
coord_sf(crs = 3083, datum = NA) +
scale_fill_manual(values = c("R" = "red", "D" = "blue")) +
theme_minimal(base_size = 16) +
labs(title = "State House Districts in Texas")
library(tidycensus)
library(tidyverse)
library(sf)
# Get dataset with geometry set to TRUE
orange_value <- get_acs(geography = "tract", state = "CA",
county = "Orange",
variables = "B25077_001",
geometry = TRUE)
Getting data from the 2012-2016 5-year ACS
Using FIPS code '06' for state 'CA'
Using FIPS code '059' for 'Orange County'
# Plot the estimate to view a map of the data
plot(orange_value["estimate"])
# Get an income dataset for Idaho by school district
idaho_income <- get_acs(geography = "school district (unified)",
variables = "B19013_001",
state = "ID")
# Get a school district dataset for Idaho
idaho_school <- school_districts(state = "ID", type = "unified", class = "sf")
# Join the income dataset to the boundaries dataset
id_school_joined <- left_join(idaho_school, idaho_income, idaho_school, by = "GEOID")
plot(id_school_joined["estimate"])
# Get a dataset of median home values from the 1-year ACS
state_value <- get_acs(geography = "state",
variables = "B25077_001",
survey = "acs1",
geometry = TRUE,
shift_geo = TRUE)
Getting data from the 2016 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using feature geometry obtained from the albersusa package
Please note: Alaska and Hawaii are being shifted and are not to scale.
# Plot the dataset to view the shifted geometry
plot(state_value["estimate"])
# Set the color guide to FALSE and add a subtitle and caption to your map
ggplot(marin_value, aes(fill = estimate, color = estimate)) +
geom_sf() +
scale_fill_viridis_c(labels = scales::dollar) +
scale_color_viridis_c(guide = FALSE) +
theme_minimal() +
coord_sf(crs = 26911, datum = NA) +
labs(title = "Median owner-occupied housing value by Census tract",
subtitle = "Marin County, California",
caption = "Data source: 2012-2016 ACS.\nData acquired with the R tidycensus package.",
fill = "ACS estimate")
# Generate point centers
centers <- st_centroid(state_value)
# Set size parameter and the size range
ggplot() +
geom_sf(data = state_value, fill = "white") +
geom_sf(data = centers, aes(size = estimate), shape = 21,
fill = "lightblue", alpha = 0.7, show.legend = "point") +
scale_size_continuous(range = c(1, 20))
Other pacakges - censusapi - full census API access, very extensive - ipumsr - historical data - cancensus - Canadian census
Other Recommendations - Working with data in the tidyverse - data visualizeation with ggplot2 - interactive maps with leaflet in R